Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3324102
{"title":"IEEE Foundation filler","authors":"","doi":"10.1109/mits.2023.3324102","DOIUrl":"https://doi.org/10.1109/mits.2023.3324102","url":null,"abstract":"","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135454851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3302330
Qing Song, Xiaolei Li, Chao Gao, Zhen Shen, Gang Xiong
Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car’s travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace.
{"title":"Optimized Multicar Dynamic Route Planning Based on Time-Hierarchical Graph Model","authors":"Qing Song, Xiaolei Li, Chao Gao, Zhen Shen, Gang Xiong","doi":"10.1109/mits.2023.3302330","DOIUrl":"https://doi.org/10.1109/mits.2023.3302330","url":null,"abstract":"Traffic congestion has become a major concern in most cities all over the world. The proper guidance of cars with an effective route planning method has become a fundamental and smart way to alleviate congestion under existing urban road facilities. Current route planning methods mainly focus on a single car, but ignoring the dynamic effect between cars may lead to severe congestion during the actual driving guidance. In this article, we extend the study of route planning to the case of multiple cars and present a novel multicar shortest travel-time routing problem. The objective is to minimize the average travel time by considering the dynamic effect of the induced traffic congestion on travel speed, while ensuring that each car’s travel distance is within an acceptable range. We construct a time-hierarchical graph model for structuring the spatiotemporal dynamic properties of the urban road network and then develop a two-level multicar route planning optimization method for complex problem solving. The experimental results show that our path recommendations reduce the average travel time by 51.74% and 38.87% on average compared to two representative methods. Our research will become more important in the years ahead as self-driving cars become more commonplace.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"177-191"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3293760
Yuxuan Guo, Xuan Pei, Xiaolin Luo, Hongjie Liu, T. Tang, Taogang Hou
Virtual coupling allows multiple trains to run synchronously in a virtually coupled formation via train-to-train (T2T) wireless communication. The distance between virtual coupling trains can be reduced greatly to improve the line capacity. However, the time delay in T2T communication cannot be avoided, and the delay may fluctuate due to the uncertainties in the train operation, which will negatively impact the virtual coupling trains. In this article, a particle swarm optimization (PSO)-based online optimization approach for virtual coupling that considers the nondeterministic delay in T2T communication is presented. The optimization objectives include energy consumption, ride comfort, operation speed, and the distance between trains. The PSO algorithm is adopted to solve the problem online so that the train can be controlled according to the optimized traction/braking acceleration. The simulation was conducted with data from the existing Beijing subway line, and the results show that the proposed method reduces the impact of nondeterministic delay on virtual coupling significantly. The speed error of the follower train is below 0.3 m/s, and the tracking distance error is below 0.8 m. Moreover, the requirements of real-time control and ride comfort are satisfied.
{"title":"A Particle Swarm Optimization-Based Online Optimization Approach for Virtual Coupling Trains With Communication Delay","authors":"Yuxuan Guo, Xuan Pei, Xiaolin Luo, Hongjie Liu, T. Tang, Taogang Hou","doi":"10.1109/mits.2023.3293760","DOIUrl":"https://doi.org/10.1109/mits.2023.3293760","url":null,"abstract":"Virtual coupling allows multiple trains to run synchronously in a virtually coupled formation via train-to-train (T2T) wireless communication. The distance between virtual coupling trains can be reduced greatly to improve the line capacity. However, the time delay in T2T communication cannot be avoided, and the delay may fluctuate due to the uncertainties in the train operation, which will negatively impact the virtual coupling trains. In this article, a particle swarm optimization (PSO)-based online optimization approach for virtual coupling that considers the nondeterministic delay in T2T communication is presented. The optimization objectives include energy consumption, ride comfort, operation speed, and the distance between trains. The PSO algorithm is adopted to solve the problem online so that the train can be controlled according to the optimized traction/braking acceleration. The simulation was conducted with data from the existing Beijing subway line, and the results show that the proposed method reduces the impact of nondeterministic delay on virtual coupling significantly. The speed error of the follower train is below 0.3 m/s, and the tracking distance error is below 0.8 m. Moreover, the requirements of real-time control and ride comfort are satisfied.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"49-63"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3296331
Wenwen Qin, Mingfeng Zhang, Wu Li, Yunyi Liang
Travel time distribution (TTD) estimation on urban arterial links with sparse trajectory data is a practically important while substantially challenging subject. Although several methods have been proposed to estimate link TTDs, the applications of the existing methods are often limited by their shortcomings, such as the needs for extra road geometric features, signal control plans, model assumptions, etc. As an alternative, this article makes full use of ubiquitous incomplete trajectories that only traverse part of the link and introduces a novel bilevel Bayesian sampling method to alleviate the data sparsity problem. The focus of this study is to develop a framework of estimating link TTDs based on incomplete and complete trajectories by using the spatiotemporal K-nearest neighbors (KNN) algorithm and Bayesian approach. Three major steps are involved: • First, we consider a straightforward trajectory imputation method for missing GPS points to improve the input data quality and serve as a basis for measuring the similarity between incomplete and complete trajectories. • Then, a spatiotemporal KNN algorithm is proposed to estimate virtual link travel times of incomplete trajectories for the purposes of increasing the travel time sample size. • Finally, a bilevel Bayesian-based sampling method comprising an improved particle filter and Gibbs sampling is introduced to approximate the posterior distribution of link travel times based on the enhanced data. A case study was conducted on a major arterial in Nanjing, China. The results indicate that the proposed approach with the augmented data can achieve promising performance compared to the competing methods in terms of effectiveness and adaptiveness.
{"title":"Spatiotemporal K-Nearest Neighbors Algorithm and Bayesian Approach for Estimating Urban Link Travel Time Distribution From Sparse GPS Trajectories","authors":"Wenwen Qin, Mingfeng Zhang, Wu Li, Yunyi Liang","doi":"10.1109/mits.2023.3296331","DOIUrl":"https://doi.org/10.1109/mits.2023.3296331","url":null,"abstract":"Travel time distribution (TTD) estimation on urban arterial links with sparse trajectory data is a practically important while substantially challenging subject. Although several methods have been proposed to estimate link TTDs, the applications of the existing methods are often limited by their shortcomings, such as the needs for extra road geometric features, signal control plans, model assumptions, etc. As an alternative, this article makes full use of ubiquitous incomplete trajectories that only traverse part of the link and introduces a novel bilevel Bayesian sampling method to alleviate the data sparsity problem. The focus of this study is to develop a framework of estimating link TTDs based on incomplete and complete trajectories by using the spatiotemporal K-nearest neighbors (KNN) algorithm and Bayesian approach. Three major steps are involved: • First, we consider a straightforward trajectory imputation method for missing GPS points to improve the input data quality and serve as a basis for measuring the similarity between incomplete and complete trajectories. • Then, a spatiotemporal KNN algorithm is proposed to estimate virtual link travel times of incomplete trajectories for the purposes of increasing the travel time sample size. • Finally, a bilevel Bayesian-based sampling method comprising an improved particle filter and Gibbs sampling is introduced to approximate the posterior distribution of link travel times based on the enhanced data. A case study was conducted on a major arterial in Nanjing, China. The results indicate that the proposed approach with the augmented data can achieve promising performance compared to the competing methods in terms of effectiveness and adaptiveness.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"152-176"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3298534
Yushan Han, Hui Zhang, Huifang Li, Yi Jin, Congyan Lang, Yidong Li
Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This article reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlooked challenges between current academic research and real-world applications.
{"title":"Collaborative Perception in Autonomous Driving: Methods, Datasets, and Challenges","authors":"Yushan Han, Hui Zhang, Huifang Li, Yi Jin, Congyan Lang, Yidong Li","doi":"10.1109/mits.2023.3298534","DOIUrl":"https://doi.org/10.1109/mits.2023.3298534","url":null,"abstract":"Collaborative perception is essential to address occlusion and sensor failure issues in autonomous driving. In recent years, theoretical and experimental investigations of novel works for collaborative perception have increased tremendously. So far, however, few reviews have focused on systematical collaboration modules and large-scale collaborative perception datasets. This article reviews recent achievements in this field to bridge this gap and motivate future research. We start with a brief overview of collaboration schemes. After that, we systematically summarize the collaborative perception methods for ideal scenarios and real-world issues. The former focuses on collaboration modules and efficiency, and the latter is devoted to addressing the problems in actual application. Furthermore, we present large-scale public datasets and summarize quantitative results on these benchmarks. Finally, we highlight gaps and overlooked challenges between current academic research and real-world applications.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"42 12","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136102741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3276592
Quang Huy Bui, J. Suhr
Even though one-stage detectors have advantages for resource-constrained and real-time applications, they face the disadvantage of mediocre performance. Thus, this article proposes a novel one-stage parking slot detection method that achieves comparable performance to two (or multi-)stage detectors. The proposed method extracts the components and properties of the parking slots from input images. As the extracted components and properties are unorganized, it is significantly important to combine them correctly. To this end, this article introduces the component linkages that provide sufficient information for connecting the extracted components and properties. By the guide of the component linkages, the components and properties of the parking slots are progressively assembled to produce precise detection results. In experiments, the proposed method was evaluated using two large-scale parking slot detection datasets and showed state-of-the-art performances. Specifically, in the Seoul National University (SNU) dataset, the proposed method achieved 96.73% recall and 96.75% precision while maintaining a fast processing speed of 134 frames per second. In addition, this article provides a new set of labels for the SNU dataset, which covers more than 60,000 parking slots with high-quality annotations.
{"title":"One-Stage Parking Slot Detection Using Component Linkage and Progressive Assembly","authors":"Quang Huy Bui, J. Suhr","doi":"10.1109/mits.2023.3276592","DOIUrl":"https://doi.org/10.1109/mits.2023.3276592","url":null,"abstract":"Even though one-stage detectors have advantages for resource-constrained and real-time applications, they face the disadvantage of mediocre performance. Thus, this article proposes a novel one-stage parking slot detection method that achieves comparable performance to two (or multi-)stage detectors. The proposed method extracts the components and properties of the parking slots from input images. As the extracted components and properties are unorganized, it is significantly important to combine them correctly. To this end, this article introduces the component linkages that provide sufficient information for connecting the extracted components and properties. By the guide of the component linkages, the components and properties of the parking slots are progressively assembled to produce precise detection results. In experiments, the proposed method was evaluated using two large-scale parking slot detection datasets and showed state-of-the-art performances. Specifically, in the Seoul National University (SNU) dataset, the proposed method achieved 96.73% recall and 96.75% precision while maintaining a fast processing speed of 134 frames per second. In addition, this article provides a new set of labels for the SNU dataset, which covers more than 60,000 parking slots with high-quality annotations.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"26 1","pages":"33-48"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.1109/mits.2023.3289969
K. Tuncel, H. Koutsopoulos, Zhenliang Ma
Urban railway systems in many cities are facing increasing levels of crowding and operating near capacity. Crowding at stations and on trains is a concern due to its impact on safety, service quality, and operating efficiency. Denied boarding is becoming a key measure of the impact of near-capacity operations on customers, and it is fundamental for calculating other performance metrics, such as the expected waiting time. Several approaches have been proposed to infer denied boarding using smart card and train movement data. They formulate the inference as a maximum-likelihood estimation problem on observed trip journey times, with an a priori model assumption on independent journey time components, and they require extensive ground truth data collection and model calibration for practical deployment. This article proposes a data-driven unsupervised clustering-based approach to robustly infer denied boarding probabilities for access-plus-waiting times by decomposing trip journey times (instead of directly on journey times). The approach is applicable to closed fare collection systems and consists of two main steps: grouping passengers to trains via trip exit information by using a probabilistic model and inferring denied boarding probabilities by using a structured mixture distribution model with physical constraints and systematic parameter initialization. The method is data driven and requires neither observations of denied boarding nor assumptions about model components’ independence and parameter calibrations. Case studies validate the proposed method by using actual data and comparing it with state-of-the-art models and survey data. The results demonstrate the proposed model’s robustness and applicability for estimating denied boarding under both normal and abnormal operation conditions.
{"title":"An Unsupervised Learning Approach for Robust Denied Boarding Probability Estimation Using Smart Card and Operation Data in Urban Railways","authors":"K. Tuncel, H. Koutsopoulos, Zhenliang Ma","doi":"10.1109/mits.2023.3289969","DOIUrl":"https://doi.org/10.1109/mits.2023.3289969","url":null,"abstract":"Urban railway systems in many cities are facing increasing levels of crowding and operating near capacity. Crowding at stations and on trains is a concern due to its impact on safety, service quality, and operating efficiency. Denied boarding is becoming a key measure of the impact of near-capacity operations on customers, and it is fundamental for calculating other performance metrics, such as the expected waiting time. Several approaches have been proposed to infer denied boarding using smart card and train movement data. They formulate the inference as a maximum-likelihood estimation problem on observed trip journey times, with an a priori model assumption on independent journey time components, and they require extensive ground truth data collection and model calibration for practical deployment. This article proposes a data-driven unsupervised clustering-based approach to robustly infer denied boarding probabilities for access-plus-waiting times by decomposing trip journey times (instead of directly on journey times). The approach is applicable to closed fare collection systems and consists of two main steps: grouping passengers to trains via trip exit information by using a probabilistic model and inferring denied boarding probabilities by using a structured mixture distribution model with physical constraints and systematic parameter initialization. The method is data driven and requires neither observations of denied boarding nor assumptions about model components’ independence and parameter calibrations. Case studies validate the proposed method by using actual data and comparing it with state-of-the-art models and survey data. The results demonstrate the proposed model’s robustness and applicability for estimating denied boarding under both normal and abnormal operation conditions.","PeriodicalId":48826,"journal":{"name":"IEEE Intelligent Transportation Systems Magazine","volume":"1 1","pages":"19-32"},"PeriodicalIF":3.6,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62345823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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